InfoSci®-Journals Annual Subscription Price for New Customers: As Low As US$ 4,950

This collection of over 175 e-journals offers unlimited access to highly-cited, forward-thinking content in full-text PDF and XML with no DRM. There are no platform or maintenance fees and a guarantee of no more than 5% increase annually.

Receive the complimentary e-books for the first, second, and third editions with the purchase of the Encyclopedia of Information Science and Technology, Fourth Edition e-book. Plus, take 20% off when purchasing directly through IGI Global's Online Bookstore.

Abstract

Fall detection is receiving significant attention in the field of preventive medicine, wellness management and assisted living, especially for the elderly. As a result, several fall detection systems are reported in the research literature or exist as commercial systems. Most of them use accelerometers and/ or gyroscopes attached on a person's body as the primary signal sources. These systems use either discrete sensors as part of a product designed specifically for this task or sensors that are embedded in mobile devices such as smartphones. The latter approach has the advantage of offering well tested and widely available communication services, e.g. for calling emergency when necessary. Nevertheless, automatic fall detection continues to present significant challenges, with the recognition of the type of fall being the most critical. The aim of this work is to introduce a human fall and activity dataset to be used in testing new detection methods, as well as performing objective comparisons between different reported algorithms for fall detection and activity recognition, based on inertial-sensor data from smartphones. The dataset contains signals recorded from the accelerometer and gyroscope sensors of a latest technology smartphone for four different types of falls and nine different activities of daily living. Utilizing this dataset, the results of an elaborate evaluation of machine learning-based fall detection and fall classification are presented and discussed in detail.

Article Preview

1. Introduction

A fall is defined as a sudden, uncontrolled and unintentional downward displacement of the body to the ground. It is evident that falls affect millions of people (especially the elderly) and may result in significant injuries (Kannus, Sievänen, Palvanen, Järvinen, & Parkkari, 2005). Moreover, injury is a leading cause of death among elderly people (Stevens, Corso, Finkelstein, & Miller, 2006). Automatic fall detection systems rely on a set of threshold values for predetermined parameters, as well as classification rules, in order to continuously process motion data, obtained from an accelerometer and/or a gyroscope, or other sensors, and to determine in near real-time if a fall event has occurred.

The utilization of mobile phones or smartphones for the provision of pervasive health care services (Hristoskova, Sakkalis, Zacharioudakis, Tsiknakis, & De Turck, 2014) provides a cost-effective and powerful solution to the well-known issue of increasing health-care needs and costs due to the growing population of elderly (Spanakis, Lelis, Chiarugi, & Chronaki, 2005 ; Spanakis et al. 2012). Various such fall detection systems already exist (Table 1) and each one of these uses a specific phone with different embedded sensors. Moreover each method is evaluated within its own testing environment and with its own data. Thus it is very difficult, if not impossible, to compare different existing approaches on their validity and effectiveness.